Courses with Gaps
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DTS 204
3
1 institution need this
At the end of the course, the students should be able to: 1. make conclusions based on statistical assumptions, models and results; 2. make inference on statistical outcomes, and real-world implications and how these out...
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Population and samples. Asymptotics. Statistical models and methodologies. Random
sampling distributions. Elementary time series analysis. Index numbers. Demographic
measures. Estimation (point and interval) and tests of hypotheses concerning population
mean and proportion (one and two sample cases). Regression and correlation. Programming
in Python computer language. Computation of mean, variance and correlation. Sorting and
ranking of data. Data Step Processing. Preparing Data for Analysis. Evaluating Quantitative
Data. Sample Size Estimation. Basic statistical computing in regression analysis and the
analysis of designed experiments. Introduction to Monte Carlo methods. Use of statistical
packages like SPSS, SAS, Minitab, GENSTAT, EPI-INFO, SYSTAT.
Lab work: Practical experiments on statistical models and methodologies. Practical exercises
on random sampling distribution methods. Practicals on test of hypothesis, population, mean,
proportion, regression and correlation analysis. Exercise on how to sort and data from different
data set. Use of SPSS for data analysis and computation.